Electrical and Electronic Engineering - Research Publications

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    HCV E1 influences the fitness landscape of E2 and may enhance escape from E2-specific antibodies
    Zhang, H ; Bull, RA ; Quadeer, AA ; McKay, MR (OXFORD UNIV PRESS, 2023-12-14)
    The Hepatitis C virus (HCV) envelope glycoprotein E1 forms a non-covalent heterodimer with E2, the main target of neutralizing antibodies. How E1-E2 interactions influence viral fitness and contribute to resistance to E2-specific antibodies remain largely unknown. We investigate this problem using a combination of fitness landscape and evolutionary modeling. Our analysis indicates that E1 and E2 proteins collectively mediate viral fitness and suggests that fitness-compensating E1 mutations may accelerate escape from E2-targeting antibodies. Our analysis also identifies a set of E2-specific human monoclonal antibodies that are predicted to be especially resilient to escape via genetic variation in both E1 and E2, providing directions for robust HCV vaccine development.
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    Reconfigurable optical crosshaul architecture for 6G radio access networks
    Tao, Y ; Ranaweera, C ; Edirisinghe, S ; Lim, C ; Nirmalathas, A ; Wosinska, L ; Song, T (Optica Publishing Group, 2023-12)
    The radio access network (RAN) architecture is undergoing a significant evolution to support the next-generation mobile networks and their emerging applications. To realize scalable and sustainable deployment and operations, RAN needs to consider the requirements of 6G and beyond wireless technologies such as ultra densification of cells, higher data rates, ubiquitous coverage, and new radio spectrum in the millimeter-wave band. This calls for a careful redesign of every aspect of RAN, including its crosshaul. The crosshaul is an important network segment in future RAN, capable of transporting diverse traffic types with varying stringent requirements within RAN. The crosshaul towards 6G is envisioned to be highly intelligent, reconfigurable, and adaptable to dynamic service requirements and network conditions. To this end, we propose a software defined network (SDN)-enabled reconfigurable optical crosshaul architecture (ROCA) that supports heterogeneous crosshaul transport technologies and dynamic functional splittings. ROCA enables efficient and intelligent control of the crosshaul data plane. The proposed architecture with a set of the next-generation RAN (NG-RAN) transport interfaces is evaluated using network models built on the ns-3 network simulator. Simulation results demonstrate the strengths and weaknesses of different crosshaul interfaces in agreement with the understanding of respective NG-RAN interfaces from the literature, which validates the modeling accuracy. We then demonstrate the reconfigurability of the architecture using a dynamic scenario with different reconfiguration strategies for meeting the user and network demands. The results indicate that ROCA serves as a scalable and flexible foundation for supporting high-capacity delay-stringent RAN that can be used in 6G and beyond wireless technologies.
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    On Distributed Nonconvex Optimisation via Modified ADMM
    Mafakheri, B ; Manton, JH ; Shames, I (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023)
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    The Effect of Fetal Heart Rate Segment Selection on Deep Learning Models for Fetal Compromise Detection
    Mendis, L ; Palaniswami, M ; Brownfoot, F ; Keenan, E (IEEE, 2023)
    Monitoring the fetal heart rate (FHR) is common practice in obstetric care to assess the risk of fetal compromise. Unfortunately, human interpretation of FHR recordings is subject to inter-observer variability with high false positive rates. To improve the performance of fetal compromise detection, deep learning methods have been proposed to automatically interpret FHR recordings. However, existing deep learning methods typically analyse a fixed-length segment of the FHR recording after removing signal gaps, where the influence of this segment selection process has not been comprehensively assessed. In this work, we develop a novel input length invariant deep learning model to determine the effect of FHR segment selection for detecting fetal compromise. Using this model, we perform five times repeated five-fold cross-validation on an open-access database of 552 FHR recordings and assess model performance for FHR segment lengths between 15 and 60 minutes. We show that the performance after removing signal gaps improves with increasing segment length from 15 minutes (AUC = 0.50) to 60 minutes (AUC = 0.74). Additionally, we demonstrate that using FHR segments without removing signal gaps achieves superior performance across signal lengths from 15 minutes (AUC = 0.68) to 60 minutes (AUC = 0.76). These results show that future works should carefully consider FHR segment selection and that removing signal gaps might contribute to the loss of valuable information.
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    Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes
    Chen, L ; Zhu, J ; Evans, J (IEEE, 2023)
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    Wearable Transmitter Coil Design for Inductive Wireless Power Transfer to Implantable Devices.
    Tai, Y-D ; Widdicombe, B ; Unnithan, RR ; Grayden, DB ; John, SE (IEEE, 2023-07)
    Wireless endovascular sensors and stimulators are emerging biomedical technologies for applications such as endovascular pressure monitoring, hyperthermia, and neural stimulations. Recently, coil-shaped stents have been proposed for inductive power transfer to endovascular devices using the stent as a receiver. However, less work has been done on the external transmitter components, so the maximum power transferable remains unknown. In this work, we design and evaluate a wearable transmitter coil that allows 50 mW power transfer in simulation.Clinical Relevance-This allows more accurate measurements and precise control of endovascular devices.
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    On Receding-Horizon Approximation in Time-Varying Optimal Control
    Sun, J ; Cantoni, M (IEEE, 2023-01-01)
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    Economic Model Predictive Control of Water Distribution Systems with Solar Energy and Batteries
    Zheng, X ; Wang, Y ; Weyer, E ; Manzie, C (IEEE, 2023-01-01)